System and method for dilemma zone mitigation at signalized intersections

ABSTRACT

A system to reduce probability of a crash in an intersection is disclosed which includes one or more way-sensor systems, the one or more sensor systems adapted to provide position and velocity vector of an object approaching the intersection on an associated path, a processing unit including configured to: determine position of the object with respect to a predefined zone of the intersection on the associated path, determine the current and future status of the associated traffic lights on the associated path of the object, predict position of the object with respect to the predefined zone and future status of the associated traffic light, if position prediction is within a predetermined threshold, modify the current status, wherein t green  is extended, if position prediction is outside of the predetermined threshold, modify the current status, wherein t green  is reduced, and repeating above steps until the object has cleared the intersection.

CROSS-REFERENCE TO RELATED APPLICATIONS

None.

STATEMENT REGARDING GOVERNMENT FUNDING

None.

TECHNICAL FIELD

The present disclosure generally relates to traffic control systems, andin particular, to a system for dilemma zone mitigation at a signalizedintersection.

BACKGROUND

This section introduces aspects that may help facilitate a betterunderstanding of the disclosure. Accordingly, these statements are to beread in this light and are not to be understood as admissions about whatis or is not prior art.

According to the Federal Highway Administration (FHWA), signalizedintersection fatalities account for approximately 27% of all totaltraffic fatalities. Of those, about 31% involve heavy vehicles, e.g.,semitrucks. In 2018 in Indiana, the rate of fatal crashes involvingheavy vehicles at signalized intersections is nearly five times higherthan crashes that do not involve a heavy vehicle. Previous studies havefound that for red light violations, heavy vehicles entered theintersection later than passenger vehicles after the end of the yellow,and were twice as likely to violate the red light than passengervehicles. They also require substantially longer stopping distancecompared to passenger vehicles due to air brake lag and brakingperformance differences between passenger vehicles and heavy vehicles.

The stop-or-go decision is made at the onset of the signal turningyellow. A dilemma zone is a span of road upstream from an intersectionwhere a vehicle can neither stop safely within the mechanical limits ofthe vehicle nor clear the intersection at the present speed of thevehicle at the onset of conflicting green. The dilemma zone is a span ofroad some distance away from the intersection which is denoted asX_(dz). X_(dz) is mathematically equal to the difference between asuccessful minimum stopping distance under maximum amount ofdeceleration (i.e., the minimum distance away from the intersection thatonce maximum deceleration rate is applied, the vehicle will successfullycome to a stop prior to reaching the intersection) and a successfulmaximum “go-able” distance (i.e., setback from the stop line) undermaximum acceleration within the applicable speed limit that ensures thevehicle can exit the intersection prior to the onset of conflictinggreen. Once a vehicle enters the dilemma zone, with today's technologiesit becomes quite challenging to predict or even assist in avoiding anaccident. Past studies have found increasing the yellow time to beeffective for mitigating the number of red-light violations. However,drivers tend to adapt to increased yellow times resulting in lowerprobabilities of stopping. Different dilemma zone boundaries for heavyvehicles and passenger vehicles would also require reconciling yellowtiming objectives for balancing efficiency and safety.

In isolated, fully-actuated high-speed rural intersections, anothersolution is to use green extension. Studies have found that approacheswith green extension systems reduced the number of red-light violations,hard braking or other evasive actions. While green extensions are notvisually detectable by the driver, one study found that drivers wereless likely to stop due to adapted expectation compared to fixed-timesystems. Since the benefit of green extension comes mainly from reducingthe exposure of vehicles to the onset of yellow, when there isconflicting demand and the maximum green time has been reached (maxout), the phase must inevitably terminate and any safety benefits arenegated. Furthermore, as the time to max out approaches, the dilemmazone protection boundary decreases. Studies in the prior art haveimplemented an approach to reduce max outs by selectively turning offdetection in anticipation of future demand to reduce the number of greenextensions. However, in each of these cases, any attempt to address thechallenge of vehicles entering the dilemma zone is reactive and notproactive as they do not consider or modify the trajectory and positionof a vehicle or traffic signal timing before a vehicle enters thedilemma zone.

Therefore, there is an unmet need for a novel approach to manageintersection traffic particularly with respect to the dilemma zone in aproactive and dynamic manner.

SUMMARY

A system to reduce probability of a crash in an intersection isdisclosed. The system includes one or more way-sensor systems associatedwith an intersection having traffic lights associated with each paththrough the intersection, the one or more sensor systems adapted toprovide position and velocity vector of an object approaching theintersection on an associated path. The system further includes aprocessing unit. The processing unit includes a memory subsystemincluding a non-transitory computer readable medium, and a computingsubsystem including a processor. The processor is configured toimplement a deterministic method of reducing probability of crash in anintersection. In particular, the processor is configured to: A)determine position of the object with respect to a predefined zone ofthe intersection on the associated path, B) determine the current andfuture status of the associated traffic lights on the associated path ofthe object, C) predict position of the object with respect to thepredefined zone and future status of the associated traffic light; D) ifposition prediction is within a predetermined threshold, modify thecurrent status of the associated traffic light based on green extension,wherein t_(green) is extended to t_(green)+Δ₁t_(g), E) if positionprediction is outside of the predetermined threshold, modify the currentstatus of the associated traffic light based on early yellow, whereint_(green) is reduced to t_(green)−Δ₂t_(g), and repeating steps A)-E)until the object has cleared the intersection.

A method of reducing probability of a crash in an intersection is alsodisclosed. The method includes receiving data from one or more sensorsystems associated with an intersection having traffic lights associatedwith each path through the intersection, the one or more sensor systemsadapted to provide position and velocity vector of an object approachingthe intersection on an associated path. The method further includes aprocessor A) determining position of the object with respect to apredefined zone of the intersection on the associated path, B)determining the current and future status of the associated trafficlights on the associated path of the object, C) predicting position ofthe object with respect to the predefined zone and future status of theassociated traffic light, D) if position prediction is within apredetermined threshold, modifying the current status of the associatedtraffic light based on green extension, wherein t_(green) is extended tot_(green)+Δ₁t_(g), E) if position prediction is outside of thepredetermined threshold, modifying the current status of the associatedtraffic light based on early yellow, wherein t_(green) is reduced tot_(green)−Δ₂t_(g); and F) repeating steps A)-E) until the object hascleared the intersection.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1a is a schematic of a traffic control system for an intersectionaccording to the present disclosure.

FIG. 1b is a timing diagram of a traffic light and modification of greenlight time, according to the present disclosure.

FIG. 1c is a schematic of an exemplary intersection for use with thesystem of the present disclosure.

FIG. 2a is a graph of matches per waypoint vs. speed in miles per hourwhich illustrates a series of radii (3 ft, 6, ft, 9 ft, and 12 ft) andnumber of matches depending on a vehicle's speed.

FIGS. 2b-2e are graphs of distance from stopbar in feet vs. time inseconds for trial runs at about 45 mph (excluding stopping for red) foreach of the radii listed in FIG. 2a for 50 ft spacings (FIG. 2 b: 3 ftradius; FIG. 2 c: 6 ft radius; FIG. 2 d: 9 ft radius; and FIG. 2 e: 12foot radius).

FIG. 3a is a graph of lag in seconds vs. speed in miles per hour fordifferent waypoint spacing which illustrates the time lag for a pair ofwaypoints with a 6 ft radius for a series of spacings.

FIG. 3b is another graph of lag measured in seconds vs. speed measuredin miles per hour which illustrates hypothetical lag curve vs. collectedfield samples.

FIG. 4 is a complex graph illustrating probability of stopping forvehicles moving at 55 miles per hour plotted against distance from thestopbar in ft.

FIG. 5a is a bar graph showing number of vehicles per hour for threedifferent categories which shows the number of Force Gap Out (FGO)estimated in the northbound direction over a 24 hour period with thenumber of vehicles prevented from dilemma zone incursion and the numbercaused in both directions.

FIG. 5b is a bar graph showing number of vehicles per hour for threedifferent categories.

FIG. 5c is a bar graph showing number of vehicles for northbound andsouthbound for three different categories.

FIG. 6 is an example of a neural network provided according to thepresent disclosure.

FIG. 7 is graph of probability of stopping vs. distance from stop lineprovided in ft.

FIG. 8 is a schematic of a computer system that can interface with thesystem of FIG. 1 a.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the embodimentsillustrated in the drawings, and specific language will be used todescribe the same. It will nevertheless be understood that no limitationof the scope of this disclosure is thereby intended.

In the present disclosure, the term “about” can allow for a degree ofvariability in a value or range, for example, within 10%, within 5%, orwithin 1% of a stated value or of a stated limit of a range.

In the present disclosure, the term “substantially” can allow for adegree of variability in a value or range, for example, within 90%,within 95%, or within 99% of a stated value or of a stated limit of arange.

A novel approach to manage intersection traffic particularly withrespect to a vehicle entering the dilemma zone in a proactive andadaptive manner is disclosed herein. Towards this end, extendingvehicular detection further upstream of an intersection is an importantaspect of the present disclosure for reductions in dilemma zoneincursions by employing an dynamic system that operates with sensors,cameras, radar-based and/or wide-area detectors while simultaneouslyimproving efficiency at intersections.

Referring to FIG. 1a , a traffic control system 100 for an intersectionaccording to the present disclosure is shown. The system 100 includesseveral components each discussed separately. At the center of thesystem 100 is a processor 108, further described in relationship withFIG. 8. The system includes vehicle data connectivity, collectivelyshown as block 102. The block 102 includes vehicle nodes 102 ₁, 102 ₂, .. . and 102 _(n). The vehicles communicate with the processor 108 viaconnected vehicle technology discussed below. Information passed on fromvehicle node 102 _(i) (where the index i is intended to be any of theindexes 1, 2, . . . n) includes speed of the vehicle, position of thevehicle based on GPS coordinates, gross vehicle weight of the vehicle,an encoded vehicle history and classification including mechanicalqualities such as braking performance, engine performance, maintenancehistory, etc., as part of enhanced vehicle data that are becomingavailable for advantageously affecting control. The data communicatedfrom these vehicle nodes 102 _(i) is continuously updated.

Included in the system 100 is also waypoints 104. Waypoints 104 includea set of waypoints 104 ₁, 104 ₂, . . . 104 _(j). A waypoint 104 _(i) cantake many forms. For example, a waypoint may be simply a marker that isused with a camera system (not shown) operating from a fixed location inorder to match a vehicle's position to a position in proximity to theintersection. Otherwise, a waypoint 104 _(i) may be a sensor thatprovides a signal when a vehicle passes or comes within proximity of it,to thereby provide a match between the vehicle's position and thewaypoint. In addition, a waypoint 104 _(i) can be a virtual waypoint. Inthat sense, there are no physical embodiments, but rather virtualpositions near the intersection. Towards that end, vehicular positionsbroadcasted from each vehicle node 102 _(i) is matched to these virtualwaypoints 104 _(i) in order to determine the relative position of eachvehicle node 102 _(i) to the intersection.

Additionally, in the system 100 is also a set of sensors 106. Thesesensors include 106 ₁ . . . 106 _(k). Sensors 106 can be loop detectors,discussed below comprising an electro-magnetic sensor that generate asignal for the processor 108 when an object (e.g., a vehicle) passesover the sensor, or ambient sensors providing road conditions, e.g.,whether the road is wet, icy, slope of the roadway (i.e., uphill,downhill or flat), magnetometers, radar, infrared. Each of the above(vehicle nodes 102, waypoints 104 and sensors represent a communicationlink to the processor 108 which is used to i) determine vehiculartraffic approaching an intersection; and ii) match and map position ofeach vehicle with respect to the intersection.

The processor 108 communicates with a traffic light controller 110 inorder to control the traffic light 112. In one embodiment, processor 108and traffic light controller 110 are the same unit. The traffic light112 includes an actuation circuit 114 (also known as load switches)which controls the light 116. The light 116 includes green, yellow, andred. There are several modes for activating the traffic light 112. Thefirst mode is a purely fixed-timed uncoordinated mode, where the trafficlights operate on the basis of a timing sequence without any concernsfor live traffic patterns. In this mode, traffic in the mainthoroughfare vs. traffic in the crossroads do not affect the timing ofthe traffic light. This mode is useful in settings where vehicle sensorinstallation and maintenance costs are prohibitive. The second is thefixed-time coordinated mode, where a plurality of lights are sequencedin order to provide sequential green lights for a plurality ofintersections. In this mode, the beginning and end of green time are setto ensure vehicles arrive at the most optimum window, i.e. on green,from adjacent intersections, however, traffic on both the thoroughfarevs. traffic in the crossroads do not affect the green light durationbased on live traffic patterns. The third mode is actuated-uncoordinatedmode, where the traffic light 112 changes based on live trafficpatterns, e.g., vehicles approaching the intersection, however, there isa minimum and maximum green for each direction, i.e., the traffic lightat each direction can stay green only within a pre-defined, limitedduration when there are vehicles at other directions competing for greentime. The fourth mode is actuated-coordinated mode, where the trafficlight 112 changes based on live traffic patterns, e.g. vehiclesapproaching the intersection, however the green time for each directionis subject to expire, i.e. force-off, based on a timer to ensurevehicles arrive at the most optimum window, i.e. on green, from adjacentintersections. An example timing of green light, yellow light, and redlight is shown in FIG. 1b , which is a timing chart showing timingrelationship between the three green, yellow, and red light at theintersection for the first mode (fixed-time uncoordinated mode). In FIG.1b , a cycle begins at t=t₀ at which point the green light at theintersection, e.g., for the major movement lanes, is activated. Theduration of the green light is shown as t=t_(G). At the termination ofthe green period (i.e., at t=t_(G)), the green light is deactivated andthe yellow light is turned on and it remain on for t=t_(Y). At thetermination of the yellow period (i.e., at t=t_(G)+t_(Y)), the lightturns red and remains red for t=t_(R).

Referring back to FIG. 1a , the processor 108 receives information abouta vehicle 102 _(i) approaching the intersection. Suppose the vehicle isa heavy vehicle, e.g., a semi-truck. The processor 108 receivesinformation such as the vehicle weight, speed of the vehicle (oralternatively, calculates the speed of the vehicle based on when thevehicle is passing by waypoints), road conditions, temperature, vehicleidentification number, and other relevant information about the vehicle102 _(i). The processor then calculates whether the vehicle cansuccessfully stop prior to reaching the intersection based on theplanned termination of the green light (see FIG. 1b , t=t_(G)). If theprocessor 108 determines that the planned termination of the green light(i.e., t=t_(G)) is insufficient for a successful stop whereby thevehicle would be caught in a dilemma zone scenario, then the processor108 operates the traffic light in the third actuated-uncoordinated mode.If the green light duration for the direction is within the pre-definedmaximum, the processor provides a signal to the traffic light controller110 to extend the green. This green extension ΔG is seen in FIG. 1b .Instead of the green cycle terminating at t=t_(G), the green periodterminates at t_(G)+ΔG. This allows the vehicle to safely pass throughthe intersection. If the green light duration can no longer be extendeddue to the anticipated reaching of the pre-defined maximum, i.e.force-off, the green time is thereby reduced by ΔG′ to allow the vehicleto safely come to a stop outside of the dilemma zone. The green periodthus terminates at t_(G)−ΔG′. The calculations performed by theprocessor may be exceedingly sophisticated by obtaining service recordsfrom the vehicle 102 _(i), taking into account road conditions, and avariety of information.

According to one embodiment of the present disclosure, the communicationbetween the vehicle 102 _(i) and the processor is a two-waycommunication. In this mode, the processor 108 not only controls thetraffic light controller 110 (e.g., by providing a green extension), theprocessor 108 also communicates with the vehicle 102 _(i) and requestthe vehicle to i) maintain speed, ii) increase speed at a definedacceleration, or iii) decrease speed at defined deceleration.Accordingly, augmentation to the traffic light is in concert with therequested change in acceleration/deceleration. This mode is particularlyuseful but not limited for autonomous or driver-less vehicles. Forexample, the requested increased acceleration is based on accessinglookup tables and performing calculations based on the vehicle, theweight, whether the vehicle is moving uphill/downhill or moving on levelground. The latter, i.e., the slope of the road, can have a significantimpact on the ability of a vehicle to successfully stop or pass throughthe intersection as earth's gravity cooperates or opposes the desiredacceleration/deceleration based on the slope of the roadway utilizingvector calculus. Once the acceleration and deceleration rate iscalculated, and communicated to the vehicle, the processor 108continually monitors the vehicle progress as the vehicle 102 _(i) ispassing by waypoints and the requested acceleration/deceleration iscontinually updated until the vehicle makes a successful stop or passesthrough the intersection. Therefore, the requested acceleration ordeceleration can be based on maximum determined calculations, or basedon a continually changing request in order to control the position ofthe vehicle 102 _(i).

In FIG. 1a , within the processor 108 is also a sub-processing block109. This sub-processing block is intended to provide a deep-learningobject for the processor 108. Towards this end, the sub-processing block109 examines how best vehicles have been able to successfully progressthrough the intersection. For example, in the above situation, if theprocessor 108 demands a specific deceleration rate from the vehicle 102_(i), and the vehicle is unable to successfully stop prior to enteringthe intersection, then the sub-processing block 109 updates its deeplearning model (e.g., a neural network). In this embodiment, theprocessor uses a weighted input from the sub-processing block 109 inorder to determine a requested acceleration/deceleration rate from thevehicle 102 _(i). More on the deep learning model of the sub-processingblock 109 is provided below.

Connected vehicle (CV) technology using Dedicated Short RangeCommunications (DSRC) protocols are utilized by the system of thepresent disclosure to detect equipped vehicles from about 1000 ft toabout 1.2 miles by a road-side unit (RSU). The Basic Safety Messages(BSMs) transmitted by the vehicles to the intersection contain latitudeand longitude, speed, elevation, heading, braking information, andtimestamp at 0.1 s interval. The system of the present disclosure usesmap-matching of BSMs to physical or virtual waypoints in lanes upstreamof a signalized intersection to determine vehicle position relative to astop bar. If dilemma zone mitigation is necessary, phase control actionusing NTCIP 1202 objects is automatically initiated. NationalTransportation Communications for ITS Protocol (NTCIP) is the standardcommunication protocol for Intelligent Transportation Systems (ITS),known by a person having ordinary skill in the art, of the datatransmission between the traffic control devices and ITS system. NTCIPis made up of five layers, which include Information Level, ApplicationLevel, Transport Level, Sub-network Level and Plant Level. Theinformation level provides definitions while the application levelincludes information about data package and the standards by which datais communicated and managed. Other layers provide information aboutrouting, communication protocol, and the physical layer.

Thus, the system of the present disclosure matches BSMs to virtualwaypoints to provide sufficient performance for dilemma zone mitigatingtactics, provides a dilemma zone mitigating tactic for CV, and evaluatethe performance of the tactic using Automated Traffic Signal PerformanceMeasures (ATSPM) data. These goals are also visited in an adaptiveneural network engine to provide an adaptive solution that continuallyimproves based on evaluation of its output vs. actual results.

A rural high-speed signalized intersection is selected for collectingdata, as schematically shown in FIG. 1c . In this example highway, themainline arterial highway has a total of four 12 ft lanes (lane 1, lane2, lane 3, and lane 4) two of which (lane 1 and lane 2) goingnorth-south and the other two (lane 3 and lane 4) going south-north andwith a speed limit of 55 mph. Lane 1 and lane 2 are schematicallyseparated from lane 3 and lane 4 with a dashed line. The cross-road is acounty road with a total of two 12 ft lanes each of which goingeast-west. The lanes in the crossroad are separated by a long dashedline. The mode distribution is 26% heavy vehicles. Loop detectors(induction-based systems which operate similar to a metal detector andthus measure a change in magnetic field when a metallic object passesover the sensor wire) are located 405 ft upstream of the stop bar inboth of the mainline approaches (callout i, callout ii) with a 5 svehicle green extension time (i.e., green can be extended from thepredetermined period by 5 seconds but remain below maximum green). Theintersection runs fully actuated with a 60 s max time on the mainlinephases.

In addition to the loop detectors, a roadside unit (RSU)-a transceiveradapted to provide vehicle to infrastructure wireless connectivity—isinstrumented atop a northeast pole facing southwest and is connected tothe same subnet as the traffic signal controller. Any BSM received isimmediately forwarded the processor 108 (see FIG. 1) where themap-matching is performed. As an example, a series of 22 virtualwaypoints with 50 ft spacing in the main through lanes are definedstarting from the stop bar up to 1,050 feet (FIG. 1c ).

In particular to dilemma zones, the onset of yellow time can be recordedand thus determine whether a phase termination is due to a gap out, maxout, or force off at 0.1 s interval. Phase termination is the end of thegreen phase. For fixed-time phase termination is based on a pre-settimer. For actuated (sensor-driven) operation, there are three reasonswhy a phase terminates: gap out (where a gap in traffic, results in theend of a phase), max out (maximum green time has been reached in anuncoordinated/free-running intersection), force-off (time has beenreached/passed the point when this movement should be green to allow forcoordination, for actuated operation). Please see below for referencefrom FHWA's signal timing manual. Additionally, vehicle detection datais recorded and can be compared to the phase termination time todetermine whether a vehicle was in the dilemma zone at the onset ofyellow.

A vehicle instrumented with a CV technology onboard unit (OBU) is usedfor sending BSMs to the RSU. The RSU forwards received packets above athreshold of −82 dbm to the embedded co-processor unit installed withinthe traffic signal controller. A set of virtual waypoints containinglatitude, longitude, a range of acceptable heading, and associated laneand phase information is preloaded on the co-processor system, where anapplication persistently listens for new BSMs. Each received packetwithin the acceptable strength threshold is decoded and matched to theset of virtual waypoints. If the vehicle sending the BSM is in proximityof a waypoint within the range of acceptable heading, a call is placedvia NTCIP for the associated phase. All BSMs, successful waypointmatches, and phase calls are logged locally on the co-processor system.

A radius is the maximum distance from each waypoint that a vehicle canbe matched. Referring to FIG. 2a , a graph of matches per waypoint vs.speed in miles per hour is provided which illustrates a series of radii(3 ft, 6, ft, 9 ft, and 12 ft) and number of matches depending on avehicle's speed. With a transmit interval of 0.1 s, a vehicle travellingat 55 mph (80.7 fps) is not guaranteed to match a waypoint with a 3 ftradius threshold (callout i in the graph), assuming the vehicle'strajectory, OBU antenna, and waypoint are all reasonably centered in thelane.

Referring to FIGS. 2b-2e , graphs of distance from stopbar in feet vs.time in seconds are provided for trial runs at about 45 mph (excludingstopping for red) for each of the radii listed in FIG. 2a for 50 ftspacings (FIG. 2 b: 3 ft radius; FIG. 2 c: 6 ft radius; FIG. 2 d: 9 ftradius; and FIG. 2 e: 12 foot radius). Out of the 22 waypoints, a 3 ftradius threshold resulted in 16 out of the 22 waypoints missed (see FIG.2b , dotted lines), while radii 6 ft and greater had no missed waypoints(dark crosses). The larger radii of 9 ft and 12 ft yielded more matchesper waypoint, but risk encroachment into adjacent lanes. If more thanone waypoint was matched, the waypoint closest to the vehicle wasselected, which performed well to exclude adjacent lanes. For thisstudy, a 6 ft radius threshold is used which covers one lane width atthe study location.

The following procedure was used to determined waypoint spacing. Thespacing is the distance between the centers of two consecutivewaypoints. As the spacing increases, the lag between matches alsoincreases because the vehicle needs to “traverse the gap.” FIG. 3a is agraph of lag in seconds vs. speed in miles per hour for differentwaypoint spacing which illustrates the time lag for a pair of waypointswith a 6 ft radius for a series of spacings. For a 12 ft spacing, everyBSM would be matched assuming vehicle and waypoints are both centered inthe lane, thus the lag is constantly 0.1 s. The tradeoff for denselypopulating waypoints is that the size of the communication message willsubstantially increase, and may cause capacity challenges under load.The lag stays below 1.0 s for spacings of 75 ft, 50 ft, and 25 ft.However, for 100 ft spacing the lag is above 1.0 s.

To evaluate the performance of waypoint spacing using 6 ft radius, 50 ftspacing is used which gives an estimated lag time of 0.59 s betweenmatches at 55 mph. Referring to FIG. 3b another graph of lag measured inseconds vs. speed measured in miles per hour is shown which illustrateshypothetical lag curve vs. collected field samples, which performsreasonably close to the estimating function. Callout i is an instancewhere three waypoints are missed by the vehicle when the vehicle is notcentered in the lane. Callout ii and callout iii are two instances wherefour BSM messages are dropped each, therefore missing a portion of onewaypoint. Callout iv is an instance where one waypoint is missed by thevehicle completely and callout v is when the vehicle veered slightly tothe edge of the lane at low speed. Callout vi shows the matches withinone waypoint which has a lag equivalent to the DSRC transmissioninterval of 0.1 s. At faster speeds beyond 50 mph, the number of matcheswithin one waypoint drops off. Overall 83% of the samples have lower lagthan the hypothetical curve and 95% of the samples are within 5% orless.

On a valid match between a vehicle's location and a waypoint, a call isplaced for an associated phase using the NTCIP phase call controlobject. To record only instances where the mainline phases (Ø2 and Ø6)are called by the BSM, they must be separated from the loop detectorcalls to the same phase. Since NTCIP does not allow calling of detectorchannels directly, two “dummy” phases (Ø9 and Ø11) of 0.1 s duration arecreated on a third and fourth timing ring that each calls a dummydetector channel. Each of the dummy detector channels then calls thetrue movement phase.

By default, when a phase using call control is set, the call latches fora deterministic amount of time defined by the unit backup timeparameter. For this study, 1 s is set for this parameter. To prevent thedummy phases from resting, buffer phases (Ø10 and Ø12) with backupprevent and recall enabled are programmed in each ring of the dummyphases, also with a 0.1 s duration.

Using the above described phase and detector programming, the BSMs areable to extend the green on a mainline phase when they are matched witha waypoint up to 1,050 ft in advance of the stop bar as shown in FIG. 1c.

This present disclosure uses Force Gap Out (FGO) to selectivelyearly-terminate mainline phases before a subject vehicle enters thedilemma zone. FGO is triggered when it is determined that a CV will bewithin dilemma zone limits at the onset of yellow.

Dilemma zone performance deteriorates steeply during peak periods due tomax outs occurring when one or more vehicles are within the dilemmazone. A binary regression model is used for estimating stopping distanceand time within these thresholds, defined by

$\begin{matrix}\frac{1}{1 + e^{{- \alpha} - {\beta_{1}V} - {\beta_{2}X}}} & (1)\end{matrix}$

where V is the velocity, andX is the stopping distance. The parameters for heavy vehicles are usedwith α=0.1, β₁=−0.1 and β₂=0.08. The estimated dilemma zone boundary iswithin 2.2 s.

Referring to FIG. 4, a complex graph is provided illustratingprobability of stopping for vehicles moving at 55 miles per hour isplotted against distance from the stopbar in ft and which illustratesthe probability of stopping curve as defined by Eq. 1. Between 10% and90% of drivers choose to stop from 269 ft to 449 ft in advance of thestop bar when the onset of yellow occurs in this boundary (callout i,callout ii). Within these limits no FGOs would be triggered. At 50%probability of stopping (callout iii), the risk of conflict is thehighest due to the same number of drivers wanting to stop as wanting toproceed.

In FIG. 4, the top horizontal axis indicates the amount of green timeleft in the phase, and the onset of yellow is aligned to the 90%threshold, corresponding to the 449 ft mark. Any CV estimated to arriveat or after this point but before the 269 ft mark (2.2 seconds later)would trigger FGO early from 449 ft to the 629 ft mark (2.2 s earlier,callout iv). An extra 0.59 s is added to this limit (callout v) toaccount for any delay due to lag between waypoints defined in theprevious section. Any FGO occurring in advance of this point is possiblebut may not be efficient.

-   -   1. In general, the FGO logic is triggered when the following        conditions are met:        -   There is a call on any side-street movement;    -   2. The max green time remaining t_(max) on phase P is less than        a critical threshold γ_(c) (2.2 s at 55 mph);    -   3. Phase P is currently on;    -   4. Phase P is called by a CV.        Once all of the above conditions are met, an alternative        detection plan is enacted immediately to gap out all mainline        phases. The logic is then blocked for t_(d) seconds, set to the        yellow and all-red clearance interval plus the min green of the        next phase. After td seconds, the detection is reverted to the        original plan.

Although a CV may prevent itself from a dilemma zone incursion usingFGO, it may cause other vehicles to be in the dilemma zone (where theywould not have been) as an effect—likely during high volume periods. Todetermine if FGO reduces dilemma zone incursions overall within aperiod, a tradeoff estimation is made using ATSPM data. FIGS. 5a-5cillustrate the result of the estimate for the study intersection of FIG.1c over a four month period. An FGO is considered in the estimation if amainline phase terminated with a max out and any detection at theadvance loop, interpolated to the onset of yellow time, falls within thedilemma zone limits. FIG. 5a which is a bar graph showing number ofvehicles per hour for three different categories which shows the numberof FGO estimated in the northbound direction over a 24 hour period withthe number of vehicles prevented from dilemma zone incursion and thenumber caused in both directions. At the northbound approach, most hourswould break even or see slightly more vehicles prevented from a dilemmazone incursion than it would have caused, with the exception of the peaktimes of 06:00, 08:00, 16:00, and 17:00.

The southbound direction shows a greater benefit (see FIG. 5b , which isa bar graph showing number of vehicles per hour for three differentcategories), as more vehicles would be prevented from a dilemma zoneincursion or at least break even for all hours. Overall, there are 310vehicles (if equipped with OBUs) estimated to trigger FGO over afour-month period, with 61 vehicles prevented in the northbounddirection and 249 vehicles prevented in the southbound direction. Thenumber of dilemma zone incursions caused by the FGOs is 173 in thenorthbound direction and 52 in the southbound direction for a total of225 FGO-caused incursions. In terms of tradeoffs, the northbounddirection had no change and the southbound direction has a net dilemmazone incursion reduction of 34%.

Returning back to FIG. 1a , as discussed above, the sub-processing block109 provides a deep-learning block for the processor 108. According toone embodiment, the sub-processing block 109 is based on neural networkused for producing and adjusting an array of binary logistic models todetermine the probability that a moving object will stop or go forexample at the onset of the yellow light.

Referring to FIG. 6, an example of a neural network is provided. Eachlayer of the neural network is defined by properties of the movingobject and conditions of the setting at the onset of yellow. Theproperties of the moving object include, but are not limited to distanceto the stop line, speed, and vehicle class. The conditions of thesetting include, but are not limited to: road condition and daylightcondition. Additionally, the final layer specifies whether the movingobject stops or goes at the onset of yellow. FIG. 6 shows the neuralnetwork's layers, with the traversal of the network taking the path ofthe aforementioned properties (i.e., distance to stop line, speed, classof vehicle, road conditions, daylight conditions, etc.). For other setsof properties and conditions may take other paths in the network. Notall properties may be available, depending on what technology isinstrumented or can be accessed, and availability of infrastructure andtechnology. Additionally, more layers may be added as technologyimproves to provide improved instrumentation.

Referring to FIG. 7, result of one binary logistic model from movingobjects and conditions with the parameters of speed as 55 miles perhour, vehicle class as Class 2, and dry daylight condition are shown.FIG. 7 provides the probability of stopping vs. distance from stop lineprovided in ft. The arrow indicates the distance of the moving object tothe stop line at the onset of yellow for the following set ofparameters: distance to stop line=455 ft; speed=55 MPH, vehicleclass=class 2; road conditions=dry; daylight conditions=daytime; andstop or go decision=stop.

Depending on whether the moving object stops or goes, the underlyingdata supporting the model is adjusted and a new model for the specificset of conditions is updated to reflect the new data input. The exampleshown in FIG. 7 at the blue arrow indicates there is a >95% probabilitya Class 2 vehicle travelling at 55 miles per hour in dry daylightconditions will stop at 455 feet upstream of the stop line at the onsetof the yellow light. The model is thus updated when actual results arecompared with model output.

Referring to FIG. 8, an example of a computer system is provided thatcan interface with the above-discussed traffic control system 100 (e.g.,see FIG. 1a ). Referring to FIG. 8, a high-level diagram showing thecomponents of an exemplary data-processing system 1000 for analyzingdata and performing other analyses described herein, and relatedcomponents. The system includes a processor 1086, a peripheral system1020, a user interface system 1030, and a data storage system 1040. Theperipheral system 1020, the user interface system 1030 and the datastorage system 1040 are communicatively connected to the processor 1086.Processor 1086 can be communicatively connected to network 1050 (shownin phantom), e.g., the Internet or a leased line, as discussed below.The imaging described in the present disclosure may be obtained usingimaging sensors 1021 and/or displayed using display units (included inuser interface system 1030) which can each include one or more ofsystems 1086, 1020, 1030, 1040, and can each connect to one or morenetwork(s) 1050. Processor 1086, and other processing devices describedherein, can each include one or more microprocessors, microcontrollers,field-programmable gate arrays (FPGAs), application-specific integratedcircuits (ASICs), programmable logic devices (PLDs), programmable logicarrays (PLAs), programmable array logic devices (PALs), or digitalsignal processors (DSPs).

Processor 1086 can implement processes of various aspects describedherein. Processor 1086 can be or include one or more device(s) forautomatically operating on data, e.g., a central processing unit (CPU),microcontroller (MCU), desktop computer, laptop computer, mainframecomputer, personal digital assistant, digital camera, cellular phone,smartphone, or any other device for processing data, managing data, orhandling data, whether implemented with electrical, magnetic, optical,biological components, or otherwise. Processor 1086 can includeHarvard-architecture components, modified-Harvard-architecturecomponents, or Von-Neumann-architecture components.

The phrase “communicatively connected” includes any type of connection,wired or wireless, for communicating data between devices or processors.These devices or processors can be located in physical proximity or not.For example, subsystems such as peripheral system 1020, user interfacesystem 1030, and data storage system 1040 are shown separately from thedata processing system 1086 but can be stored completely or partiallywithin the data processing system 1086.

The peripheral system 1020 can include one or more devices configured toprovide digital content records to the processor 1086. For example, theperipheral system 1020 can include digital still cameras, digital videocameras, cellular phones, or other data processors. The processor 1086,upon receipt of digital content records from a device in the peripheralsystem 1020, can store such digital content records in the data storagesystem 1040.

The user interface system 1030 can include a mouse, a keyboard, anothercomputer (connected, e.g., via a network or a null-modem cable), or anydevice or combination of devices from which data is input to theprocessor 1086. The user interface system 1030 also can include adisplay device, a processor-accessible memory, or any device orcombination of devices to which data is output by the processor 1086.The user interface system 1030 and the data storage system 1040 canshare a processor-accessible memory.

In various aspects, processor 1086 includes or is connected tocommunication interface 1015 that is coupled via network link 1016(shown in phantom) to network 1050. For example, communication interface1015 can include an integrated services digital network (ISDN) terminaladapter or a modem to communicate data via a telephone line or fiberoptics; a network interface to communicate data via a local-area network(LAN), e.g., an Ethernet LAN, or wide-area network (WAN); or a radio tocommunicate data via a wireless link, e.g., WiFi or GSM. Communicationinterface 1015 sends and receives electrical, electromagnetic or opticalsignals that carry digital or analog data streams representing varioustypes of information across network link 1016 to network 1050. Networklink 1016 can be connected to network 1050 via a switch, gateway, hub,router, or other networking device.

Processor 1086 can send messages and receive data, including programcode, through network 1050, network link 1016 and communicationinterface 1015. For example, a server can store requested code for anapplication program (e.g., a JAVA applet) on a tangible non-volatilecomputer-readable storage medium to which it is connected. The servercan retrieve the code from the medium and transmit it through network1050 to communication interface 1015. The received code can be executedby processor 1086 as it is received, or stored in data storage system1040 for later execution.

Data storage system 1040 can include or be communicatively connectedwith one or more processor-accessible memories configured to storeinformation. The memories can be, e.g., within a chassis or as parts ofa distributed system. The phrase “processor-accessible memory” isintended to include any data storage device to or from which processor1086 can transfer data (using appropriate components of peripheralsystem 1020), whether volatile or nonvolatile; removable or fixed;electronic, magnetic, optical, chemical, mechanical, or otherwise.Exemplary processor-accessible memories include but are not limited to:registers, floppy disks, hard disks, tapes, bar codes, Compact Discs,DVDs, read-only memories (ROM), erasable programmable read-only memories(EPROM, EEPROM, or Flash), and random-access memories (RAMs). One of theprocessor-accessible memories in the data storage system 1040 can be atangible non-transitory computer-readable storage medium, i.e., anon-transitory device or article of manufacture that participates instoring instructions that can be provided to processor 1086 forexecution.

In an example, data storage system 1040 includes code memory 1041, e.g.,a RAM, and disk 1043, e.g., a tangible computer-readable rotationalstorage device such as a hard drive. Computer program instructions areread into code memory 1041 from disk 1043. Processor 1086 then executesone or more sequences of the computer program instructions loaded intocode memory 1041, as a result performing process steps described herein.In this way, processor 1086 carries out a computer implemented process.For example, steps of methods described herein, blocks of the flowchartillustrations or block diagrams herein, and combinations of those, canbe implemented by computer program instructions. Code memory 1041 canalso store data, or can store only code.

Various aspects described herein may be embodied as systems or methods.Accordingly, various aspects herein may take the form of an entirelyhardware aspect, an entirely software aspect (including firmware,resident software, micro-code, etc.), or an aspect combining softwareand hardware aspects. These aspects can all generally be referred toherein as a “service,” “circuit,” “circuitry,” “module,” or “system.”

Furthermore, various aspects herein may be embodied as computer programproducts including computer readable program code stored on a tangiblenon-transitory computer readable medium. Such a medium can bemanufactured as is conventional for such articles, e.g., by pressing aCD-ROM. The program code includes computer program instructions that canbe loaded into processor 1086 (and possibly also other processors), tocause functions, acts, or operational steps of various aspects herein tobe performed by the processor 1086 (or other processors). Computerprogram code for carrying out operations for various aspects describedherein may be written in any combination of one or more programminglanguage(s), and can be loaded from disk 1043 into code memory 1041 forexecution. The program code may execute, e.g., entirely on processor1086, partly on processor 1086 and partly on a remote computer connectedto network 1050, or entirely on the remote computer.

Those having ordinary skill in the art will recognize that numerousmodifications can be made to the specific implementations describedabove. The implementations should not be limited to the particularlimitations described. Other implementations may be possible.

1. A system to reduce probability of a crash in an intersection,comprising: one or more way-sensor systems associated with anintersection having traffic lights associated with each path through theintersection, the one or more sensor systems adapted to provide positionand velocity vector of an object approaching the intersection on anassociated path; a processing unit including: a memory subsystemincluding a non-transitory computer readable medium, and a computingsubsystem including a processor, the processor configured to implement adeterministic method of reducing probability of crash in anintersection, the processor configured to: A) determine position of theobject with respect to a predefined zone of the intersection on theassociated path; B) determine the current and future status of theassociated traffic lights on the associated path of the object; C)predict position of the object with respect to the predefined zone andfuture status of the associated traffic light; D) if position predictionis within a predetermined threshold, modify the current status of theassociated traffic light based on green extension, wherein t_(green) isextended to t_(green)+Δ₁t_(g); E) if position prediction is outside ofthe predetermined threshold, modify the current status of the associatedtraffic light based on early yellow, wherein t_(green) is reduced tot_(green)−Δ₂t_(g); and F) repeating steps A)-E) until the object hascleared the intersection.
 2. The system of claim 1, wherein the objectis one or more of a car, a truck, a heavy duty truck, a semitruck, abicycle, a motorcycle, a scooter, a skateboard, and a pedestrian.
 3. Thesystem of claim 1, wherein the one or more sensor systems include one ormore way sensors proximate to the intersection, one or more cameras ator proximate to the intersection, global positioning systems coupled tothe object, one or more cellular communication devices, one or moreBluetooth communication devices, one or more Wi-Fi communicationdevices, one or more short-range radio frequency communication devices,and a combination thereof.
 4. The system of claim 1, wherein theposition prediction is calculated by extrapolating position data basedon the sensed position and velocity vectors.
 5. The system of claim 1,wherein modification of the current status of the associated trafficlight is based on one of a decision tree, statistical model, neuralnetwork, database function, and a combination thereof.
 6. The system ofclaim 1, the processor further configured to communicate with acorresponding processor in the object to request maintaining velocity onthe associated path until the object has passed the intersection.
 7. Thesystem of claim 6, the processor further configured to communicate witha corresponding processor in the object to change speed at a predefinedrate.
 8. The system of claim 7, wherein the predefined zone includes afirst zone, a second zone, and a third zone in order of distance fromthe intersection, wherein the processor is further configured to updatemodification of the current status of the associated traffic light basedon position and velocity vectors of the object within each of the first,second, and third zones.
 9. The system of claim 1, the processor and thememory are positioned i) proximate to the intersection and/or ii) remoteto the intersection in a computing cloud.
 10. The system of claim 1, theprocessor is further configured to update the predetermined thresholdusing an error minimization procedure, whereby error is defined as thepredicted outcome vs. measured outcome of the object position.
 11. Amethod of reducing probability of a crash in an intersection,comprising: receiving data from one or more sensor systems associatedwith an intersection having traffic lights associated with each paththrough the intersection, the one or more sensor systems adapted toprovide position and velocity vector of an object approaching theintersection on an associated path; a processor: A) determining positionof the object with respect to a predefined zone of the intersection onthe associated path; B) determining the current and future status of theassociated traffic lights on the associated path of the object; C)predicting position of the object with respect to the predefined zoneand future status of the associated traffic light; D) if positionprediction is within a predetermined threshold, modifying the currentstatus of the associated traffic light based on green extension, whereint_(green) is extended to t_(green)+Δ₁t_(g); E) if position prediction isoutside of the predetermined threshold, modifying the current status ofthe associated traffic light based on early yellow, wherein t_(green) isreduced to t_(green)−Δ₂t_(g); and F) repeating steps A)-E) until theobject has cleared the intersection.
 12. The method of claim 11, whereinthe object is one or more of a car, a truck, a heavy duty truck, asemitruck, a bicycle, a motorcycle, a scooter, a skateboard, and apedestrian.
 13. The method of claim 11, wherein the one or more sensorsystems include one or more way sensors proximate to the intersection,one or more cameras at or proximate to the intersection, globalpositioning systems coupled to the object, one or more cellularcommunication devices, one or more Bluetooth communication devices, oneor more Wi-Fi communication devices, one or more short-range radiofrequency communication devices, and a combination thereof.
 14. Themethod of claim 11, wherein the predicting position is calculated byextrapolating position data based on the sensed position and velocityvectors.
 15. The method of claim 11, wherein modifying of the currentstatus of the associated traffic light is based on one of a decisiontree, statistical model, neural network, database function, and acombination thereof.
 16. The method of claim 11, further configured tocommunicating with a corresponding processor in the object to requestmaintaining velocity on the associated path until the object has passedthe intersection.
 17. The method of claim 16, further configured tocommunicating with a corresponding processor in the object to changespeed at a predefined rate.
 18. The method of claim 17, wherein thepredefined zone includes a first zone, a second zone, and a third zonein order of distance from the intersection, and further configured toupdate modifying of the current status of the associated traffic lightbased on position and velocity vectors of the object within each of thefirst, second, and third zones.
 19. The method of claim 11, theprocessor is positioned i) proximate to the intersection or ii) remoteto the intersection in a computing cloud.
 20. The method of claim 11,further configured to updating the predetermined threshold using anerror minimization procedure, whereby error is defined as the predictedoutcome vs. measured outcome of the object position.